Today's DL networks are dominated by feedforward neural networks, i.e.,
information moves in only one forward direction during the inference
(i.e., from input nodes, through hidden nodes, and to output nodes).
They do not have cycles or loops. Their filter weights are obtained by
end-to-end optimization using the backpropagation algorithm. One
drawback of feedforward neural networks is that it is easy to add small
perturbations to the input to fool DL. This is known as the adversarial
attack [1]}. Adversarial training
[2]} has been proposed to improve the robustness of
DL networks albeit at the expense of poorer performance against clean
data.
